




"""simple docstring"""




import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def        a__					(							snake_case__			,					snake_case__			,					snake_case__							)		->   int:
			if isinstance(snake_case__			,					torch.Tensor							):
						return image
			elif isinstance(snake_case__			,					PIL.Image.Image							):
						lowerCamelCase      =   [image]

			if isinstance(image[0]			,					PIL.Image.Image							):
						lowerCamelCase      =   [np.array(i.resize((w, h)			,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
						lowerCamelCase      =   np.concatenate(snake_case__			,					axis=0							)
						lowerCamelCase      =   np.array(snake_case__							).astype(np.floataa							) / 255.0
						lowerCamelCase      =   image.transpose(0			,					3			,					1			,					2							)
						lowerCamelCase      =   2.0 * image - 1.0
						lowerCamelCase      =   torch.from_numpy(snake_case__							)
			elif isinstance(image[0]			,					torch.Tensor							):
						lowerCamelCase      =   torch.cat(snake_case__			,					dim=0							)
			return image



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__=0.9995							)		->   Optional[int]:
			if not isinstance(snake_case__			,					np.ndarray							):
						lowerCamelCase      =   True
						lowerCamelCase      =   va.device
						lowerCamelCase      =   va.cpu().numpy()
						lowerCamelCase      =   va.cpu().numpy()

			lowerCamelCase      =   np.sum(va * va / (np.linalg.norm(snake_case__							) * np.linalg.norm(snake_case__							))							)
			if np.abs(snake_case__							) > DOT_THRESHOLD:
						lowerCamelCase      =   (1 - t) * va + t * va
			else:
						lowerCamelCase      =   np.arccos(snake_case__							)
						lowerCamelCase      =   np.sin(snake_case__							)
						lowerCamelCase      =   theta_a * t
						lowerCamelCase      =   np.sin(snake_case__							)
						lowerCamelCase      =   np.sin(theta_a - theta_t							) / sin_theta_a
						lowerCamelCase      =   sin_theta_t / sin_theta_a
						lowerCamelCase      =   sa * va + sa * va

			if inputs_are_torch:
						lowerCamelCase      =   torch.from_numpy(snake_case__							).to(snake_case__							)

			return va



def        a__					(							snake_case__			,					snake_case__							)		->   Tuple:
			lowerCamelCase      =   F.normalize(snake_case__			,					dim=-1							)
			lowerCamelCase      =   F.normalize(snake_case__			,					dim=-1							)
			return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)



def        a__					(							snake_case__			,					snake_case__							)		->   Dict:
			for param in model.parameters():
						lowerCamelCase      =   value






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a=None						,					_a=None						,					_a=None						,					):





							"""simple docstring"""
							super().__init__()
							self.register_modules(
							    vae=_a						,					text_encoder=_a						,					clip_model=_a						,					tokenizer=_a						,					unet=_a						,					scheduler=_a						,					feature_extractor=_a						,					coca_model=_a						,					coca_tokenizer=_a						,					coca_transform=_a						,					)
							lowerCamelCase      =   (
							    feature_extractor.size
							    if isinstance(feature_extractor.size						,					_a					)
							    else feature_extractor.size["""shortest_edge"""]
							)
							lowerCamelCase      =   transforms.Normalize(mean=feature_extractor.image_mean						,					std=feature_extractor.image_std					)
							set_requires_grad(self.text_encoder						,					_a					)
							set_requires_grad(self.clip_model						,					_a					)





				def   _lowerCAmelCase							(			self						,					_a = "auto"					):





							"""simple docstring"""
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										lowerCamelCase      =   self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self.enable_attention_slicing(_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							set_requires_grad(self.vae						,					_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							set_requires_grad(self.vae						,					_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							set_requires_grad(self.unet						,					_a					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							set_requires_grad(self.unet						,					_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							# get the original timestep using init_timestep
							lowerCamelCase      =   min(int(num_inference_steps * strength					)						,					_a					)

							lowerCamelCase      =   max(num_inference_steps - init_timestep						,					0					)
							lowerCamelCase      =   self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a=None					):





							"""simple docstring"""
							if not isinstance(_a						,					torch.Tensor					):
										raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(_a					)}'					)

							lowerCamelCase      =   image.to(device=_a						,					dtype=_a					)

							if isinstance(_a						,					_a					):
										lowerCamelCase      =   [
										    self.vae.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(_a					)
										]
										lowerCamelCase      =   torch.cat(_a						,					dim=0					)
							else:
										lowerCamelCase      =   self.vae.encode(_a					).latent_dist.sample(_a					)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							lowerCamelCase      =   0.18_215 * init_latents
							lowerCamelCase      =   init_latents.repeat_interleave(_a						,					dim=0					)

							lowerCamelCase      =   randn_tensor(init_latents.shape						,					generator=_a						,					device=_a						,					dtype=_a					)

							# get latents
							lowerCamelCase      =   self.scheduler.add_noise(_a						,					_a						,					_a					)
							lowerCamelCase      =   init_latents

							return latents





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   self.coca_transform(_a					).unsqueeze(0					)
							with torch.no_grad(), torch.cuda.amp.autocast():
										lowerCamelCase      =   self.coca_model.generate(transformed_image.to(device=self.device						,					dtype=self.coca_model.dtype					)					)
							lowerCamelCase      =   self.coca_tokenizer.decode(generated[0].cpu().numpy()					)
							return generated.split("""<end_of_text>"""					)[0].replace("""<start_of_text>"""						,					""""""					).rstrip(""" .,"""					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   self.feature_extractor.preprocess(_a					)
							lowerCamelCase      =   torch.from_numpy(clip_image_input["""pixel_values"""][0]					).unsqueeze(0					).to(self.device					).half()
							lowerCamelCase      =   self.clip_model.get_image_features(_a					)
							lowerCamelCase      =   image_embeddings_clip / image_embeddings_clip.norm(p=2						,					dim=-1						,					keepdim=_a					)
							lowerCamelCase      =   image_embeddings_clip.repeat_interleave(_a						,					dim=0					)
							return image_embeddings_clip





				@torch.enable_grad()
				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   latents.detach().requires_grad_()

							lowerCamelCase      =   self.scheduler.scale_model_input(_a						,					_a					)

							# predict the noise residual
							lowerCamelCase      =   self.unet(_a						,					_a						,					encoder_hidden_states=_a					).sample

							if isinstance(self.scheduler						,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)					):
										lowerCamelCase      =   self.scheduler.alphas_cumprod[timestep]
										lowerCamelCase      =   1 - alpha_prod_t
										# compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										lowerCamelCase      =   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

										lowerCamelCase      =   torch.sqrt(_a					)
										lowerCamelCase      =   pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler						,					_a					):
										lowerCamelCase      =   self.scheduler.sigmas[index]
										lowerCamelCase      =   latents - sigma * noise_pred
							else:
										raise ValueError(f'scheduler type {type(self.scheduler					)} not supported'					)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							lowerCamelCase      =   1 / 0.18_215 * sample
							lowerCamelCase      =   self.vae.decode(_a					).sample
							lowerCamelCase      =   (image / 2 + 0.5).clamp(0						,					1					)

							lowerCamelCase      =   transforms.Resize(self.feature_extractor_size					)(_a					)
							lowerCamelCase      =   self.normalize(_a					).to(latents.dtype					)

							lowerCamelCase      =   self.clip_model.get_image_features(_a					)
							lowerCamelCase      =   image_embeddings_clip / image_embeddings_clip.norm(p=2						,					dim=-1						,					keepdim=_a					)

							lowerCamelCase      =   spherical_dist_loss(_a						,					_a					).mean() * clip_guidance_scale

							lowerCamelCase      =   -torch.autograd.grad(_a						,					_a					)[0]

							if isinstance(self.scheduler						,					_a					):
										lowerCamelCase      =   latents.detach() + grads * (sigma**2)
										lowerCamelCase      =   noise_pred_original
							else:
										lowerCamelCase      =   noise_pred_original - torch.sqrt(_a					) * grads
							return noise_pred, latents





				@torch.no_grad()
				def __call__(			self						,					_a						,					_a						,					_a = None						,					_a = None						,					_a = 512						,					_a = 512						,					_a = 0.6						,					_a = 50						,					_a = 7.5						,					_a = 1						,					_a = 0.0						,					_a = 100						,					_a = None						,					_a = "pil"						,					_a = True						,					_a = 0.8						,					_a = 0.1						,					_a = 0.1						,					):





							"""simple docstring"""
							if isinstance(_a						,					_a					) and len(_a					) != batch_size:
										raise ValueError(f'You have passed {batch_size} batch_size, but only {len(_a					)} generators.'					)

							if height % 8 != 0 or width % 8 != 0:
										raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'					)

							if isinstance(_a						,					torch.Generator					) and batch_size > 1:
										lowerCamelCase      =   [generator] + [None] * (batch_size - 1)

							lowerCamelCase      =   [
							    ("""model""", self.coca_model is None),
							    ("""tokenizer""", self.coca_tokenizer is None),
							    ("""transform""", self.coca_transform is None),
							]
							lowerCamelCase      =   [x[0] for x in coca_is_none if x[1]]
							lowerCamelCase      =   """, """.join(_a					)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
										if len(_a					):
													raise ValueError(
													    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
													    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'					)
										lowerCamelCase      =   self.get_image_description(_a					)
							if style_prompt is None:
										if len(_a					):
													raise ValueError(
													    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
													    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'					)
										lowerCamelCase      =   self.get_image_description(_a					)

							# get prompt text embeddings for content and style
							lowerCamelCase      =   self.tokenizer(
							    _a						,					padding="""max_length"""						,					max_length=self.tokenizer.model_max_length						,					truncation=_a						,					return_tensors="""pt"""						,					)
							lowerCamelCase      =   self.text_encoder(content_text_input.input_ids.to(self.device					)					)[0]

							lowerCamelCase      =   self.tokenizer(
							    _a						,					padding="""max_length"""						,					max_length=self.tokenizer.model_max_length						,					truncation=_a						,					return_tensors="""pt"""						,					)
							lowerCamelCase      =   self.text_encoder(style_text_input.input_ids.to(self.device					)					)[0]

							lowerCamelCase      =   slerp(_a						,					_a						,					_a					)

							# duplicate text embeddings for each generation per prompt
							lowerCamelCase      =   text_embeddings.repeat_interleave(_a						,					dim=0					)

							# set timesteps
							lowerCamelCase      =   """offset""" in set(inspect.signature(self.scheduler.set_timesteps					).parameters.keys()					)
							lowerCamelCase      =   {}
							if accepts_offset:
										lowerCamelCase      =   1

							self.scheduler.set_timesteps(_a						,					**_a					)
							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							self.scheduler.timesteps.to(self.device					)

							lowerCamelCase ,  lowerCamelCase      =   self.get_timesteps(_a						,					_a						,					self.device					)
							lowerCamelCase      =   timesteps[:1].repeat(_a					)

							# Preprocess image
							lowerCamelCase      =   preprocess(_a						,					_a						,					_a					)
							lowerCamelCase      =   self.prepare_latents(
							    _a						,					_a						,					_a						,					text_embeddings.dtype						,					self.device						,					_a					)

							lowerCamelCase      =   preprocess(_a						,					_a						,					_a					)
							lowerCamelCase      =   self.prepare_latents(
							    _a						,					_a						,					_a						,					text_embeddings.dtype						,					self.device						,					_a					)

							lowerCamelCase      =   slerp(_a						,					_a						,					_a					)

							if clip_guidance_scale > 0:
										lowerCamelCase      =   self.get_clip_image_embeddings(_a						,					_a					)
										lowerCamelCase      =   self.get_clip_image_embeddings(_a						,					_a					)
										lowerCamelCase      =   slerp(
										    _a						,					_a						,					_a					)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							lowerCamelCase      =   guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
										lowerCamelCase      =   content_text_input.input_ids.shape[-1]
										lowerCamelCase      =   self.tokenizer([""""""]						,					padding="""max_length"""						,					max_length=_a						,					return_tensors="""pt"""					)
										lowerCamelCase      =   self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
										# duplicate unconditional embeddings for each generation per prompt
										lowerCamelCase      =   uncond_embeddings.repeat_interleave(_a						,					dim=0					)

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										lowerCamelCase      =   torch.cat([uncond_embeddings, text_embeddings]					)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							lowerCamelCase      =   (batch_size, self.unet.config.in_channels, height // 8, width // 8)
							lowerCamelCase      =   text_embeddings.dtype
							if latents is None:
										if self.device.type == "mps":
													# randn does not work reproducibly on mps
													lowerCamelCase      =   torch.randn(_a						,					generator=_a						,					device="""cpu"""						,					dtype=_a					).to(
													    self.device					)
										else:
													lowerCamelCase      =   torch.randn(_a						,					generator=_a						,					device=self.device						,					dtype=_a					)
							else:
										if latents.shape != latents_shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'					)
										lowerCamelCase      =   latents.to(self.device					)

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCamelCase      =   latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowerCamelCase      =   """eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
							lowerCamelCase      =   {}
							if accepts_eta:
										lowerCamelCase      =   eta

							# check if the scheduler accepts generator
							lowerCamelCase      =   """generator""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
							if accepts_generator:
										lowerCamelCase      =   generator

							with self.progress_bar(total=_a					):
										for i, t in enumerate(_a					):
													# expand the latents if we are doing classifier free guidance
													lowerCamelCase      =   torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
													lowerCamelCase      =   self.scheduler.scale_model_input(_a						,					_a					)

													# predict the noise residual
													lowerCamelCase      =   self.unet(_a						,					_a						,					encoder_hidden_states=_a					).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																lowerCamelCase ,  lowerCamelCase      =   noise_pred.chunk(2					)
																lowerCamelCase      =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																lowerCamelCase      =   (
																    text_embeddings.chunk(2					)[1] if do_classifier_free_guidance else text_embeddings
																)
																lowerCamelCase ,  lowerCamelCase      =   self.cond_fn(
																    _a						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a						,					)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase      =   self.scheduler.step(_a						,					_a						,					_a						,					**_a					).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							lowerCamelCase      =   1 / 0.18_215 * latents
							lowerCamelCase      =   self.vae.decode(_a					).sample

							lowerCamelCase      =   (image / 2 + 0.5).clamp(0						,					1					)
							lowerCamelCase      =   image.cpu().permute(0						,					2						,					3						,					1					).numpy()

							if output_type == "pil":
										lowerCamelCase      =   self.numpy_to_pil(_a					)

							if not return_dict:
										return (image, None)

							return StableDiffusionPipelineOutput(images=_a						,					nsfw_content_detected=_a					)









"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     Union[str, Any]					=				{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"mctct"





				def __init__(			self						,					_a=8_065						,					_a=1_536						,					_a=36						,					_a=6_144						,					_a=4						,					_a=384						,					_a=920						,					_a=1e-5						,					_a=0.3						,					_a="relu"						,					_a=0.02						,					_a=0.3						,					_a=0.3						,					_a=1						,					_a=0						,					_a=2						,					_a=1						,					_a=0.3						,					_a=1						,					_a=(7,)						,					_a=(3,)						,					_a=80						,					_a=1						,					_a=None						,					_a="sum"						,					_a=False						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a						,					pad_token_id=_a						,					bos_token_id=_a						,					eos_token_id=_a					)
							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   attention_head_dim
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   layerdrop
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   pad_token_id
							lowerCamelCase      =   bos_token_id
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   conv_glu_dim
							lowerCamelCase      =   conv_dropout
							lowerCamelCase      =   num_conv_layers
							lowerCamelCase      =   input_feat_per_channel
							lowerCamelCase      =   input_channels
							lowerCamelCase      =   conv_channels
							lowerCamelCase      =   ctc_loss_reduction
							lowerCamelCase      =   ctc_zero_infinity

							# prevents config testing fail with exporting to json
							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   list(_a					)

							if len(self.conv_kernel					) != self.num_conv_layers:
										raise ValueError(
										    """Configuration for convolutional module is incorrect. """
										    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
										    f'but is `len(config.conv_kernel) = {len(self.conv_kernel					)}`, '
										    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)



